Overview

Dataset statistics

Number of variables19
Number of observations45528
Missing cells2057
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.7 MiB
Average record size in memory476.9 B

Variable types

Categorical8
Numeric9
Boolean2

Alerts

customer_id has a high cardinality: 45528 distinct values High cardinality
name has a high cardinality: 4010 distinct values High cardinality
no_of_children is highly correlated with total_family_membersHigh correlation
net_yearly_income is highly correlated with credit_limitHigh correlation
total_family_members is highly correlated with no_of_childrenHigh correlation
credit_limit is highly correlated with net_yearly_incomeHigh correlation
prev_defaults is highly correlated with default_in_last_6months and 1 other fieldsHigh correlation
default_in_last_6months is highly correlated with prev_defaults and 1 other fieldsHigh correlation
credit_card_default is highly correlated with prev_defaults and 1 other fieldsHigh correlation
no_of_children is highly correlated with total_family_membersHigh correlation
net_yearly_income is highly correlated with credit_limitHigh correlation
total_family_members is highly correlated with no_of_childrenHigh correlation
credit_limit is highly correlated with net_yearly_incomeHigh correlation
credit_score is highly correlated with credit_card_defaultHigh correlation
prev_defaults is highly correlated with default_in_last_6months and 1 other fieldsHigh correlation
default_in_last_6months is highly correlated with prev_defaults and 1 other fieldsHigh correlation
credit_card_default is highly correlated with credit_score and 2 other fieldsHigh correlation
no_of_children is highly correlated with total_family_membersHigh correlation
net_yearly_income is highly correlated with credit_limitHigh correlation
total_family_members is highly correlated with no_of_childrenHigh correlation
credit_limit is highly correlated with net_yearly_incomeHigh correlation
prev_defaults is highly correlated with default_in_last_6months and 1 other fieldsHigh correlation
default_in_last_6months is highly correlated with prev_defaults and 1 other fieldsHigh correlation
credit_card_default is highly correlated with prev_defaults and 1 other fieldsHigh correlation
credit_card_default is highly correlated with default_in_last_6months and 1 other fieldsHigh correlation
default_in_last_6months is highly correlated with credit_card_default and 1 other fieldsHigh correlation
prev_defaults is highly correlated with credit_card_default and 1 other fieldsHigh correlation
gender is highly correlated with occupation_typeHigh correlation
no_of_children is highly correlated with total_family_membersHigh correlation
net_yearly_income is highly correlated with credit_limitHigh correlation
no_of_days_employed is highly correlated with occupation_typeHigh correlation
occupation_type is highly correlated with gender and 1 other fieldsHigh correlation
total_family_members is highly correlated with no_of_childrenHigh correlation
credit_limit is highly correlated with net_yearly_incomeHigh correlation
credit_limit_used(%) is highly correlated with credit_card_defaultHigh correlation
credit_score is highly correlated with prev_defaults and 2 other fieldsHigh correlation
prev_defaults is highly correlated with credit_score and 2 other fieldsHigh correlation
default_in_last_6months is highly correlated with credit_score and 2 other fieldsHigh correlation
credit_card_default is highly correlated with credit_limit_used(%) and 3 other fieldsHigh correlation
owns_car has 547 (1.2%) missing values Missing
no_of_children has 774 (1.7%) missing values Missing
no_of_days_employed has 463 (1.0%) missing values Missing
net_yearly_income is highly skewed (γ1 = 203.6835038) Skewed
credit_limit is highly skewed (γ1 = 200.3871671) Skewed
customer_id is uniformly distributed Uniform
customer_id has unique values Unique
no_of_children has 31241 (68.6%) zeros Zeros

Reproduction

Analysis started2021-12-14 18:08:23.667019
Analysis finished2021-12-14 18:08:52.691728
Duration29.02 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

customer_id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct45528
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
CST_115179
 
1
CST_115723
 
1
CST_156690
 
1
CST_136647
 
1
CST_120077
 
1
Other values (45523)
45523 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45528 ?
Unique (%)100.0%

Sample

1st rowCST_115179
2nd rowCST_121920
3rd rowCST_109330
4th rowCST_128288
5th rowCST_151355

Common Values

ValueCountFrequency (%)
CST_1151791
 
< 0.1%
CST_1157231
 
< 0.1%
CST_1566901
 
< 0.1%
CST_1366471
 
< 0.1%
CST_1200771
 
< 0.1%
CST_1233821
 
< 0.1%
CST_1346901
 
< 0.1%
CST_1340801
 
< 0.1%
CST_1228921
 
< 0.1%
CST_1276521
 
< 0.1%
Other values (45518)45518
> 99.9%

Length

2021-12-14T23:38:52.788678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cst_1151791
 
< 0.1%
cst_1459991
 
< 0.1%
cst_1513321
 
< 0.1%
cst_1537731
 
< 0.1%
cst_1093301
 
< 0.1%
cst_1282881
 
< 0.1%
cst_1513551
 
< 0.1%
cst_1232681
 
< 0.1%
cst_1275021
 
< 0.1%
cst_1517221
 
< 0.1%
Other values (45518)45518
> 99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

name
Categorical

HIGH CARDINALITY

Distinct4010
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Jonathan
 
207
Jonathan Stempel
 
192
David
 
170
Stempel
 
165
Jessica
 
162
Other values (4005)
44632 

Length

Max length27
Median length8
Mean length8.746397821
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1226 ?
Unique (%)2.7%

Sample

1st rowita Bose
2nd rowAlper Jonathan
3rd rowUmesh Desai
4th rowRie
5th rowMcCool

Common Values

ValueCountFrequency (%)
Jonathan207
 
0.5%
Jonathan Stempel192
 
0.4%
David170
 
0.4%
Stempel165
 
0.4%
Jessica162
 
0.4%
Sarah147
 
0.3%
Lucia142
 
0.3%
Nick140
 
0.3%
Lucia Mutikani137
 
0.3%
Jones133
 
0.3%
Other values (4000)43933
96.5%

Length

2021-12-14T23:38:52.964246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jonathan591
 
0.9%
david534
 
0.8%
sarah386
 
0.6%
john381
 
0.6%
jessica380
 
0.6%
stempel358
 
0.6%
jones350
 
0.6%
paul339
 
0.5%
kim335
 
0.5%
nick334
 
0.5%
Other values (2369)59643
93.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Real number (ℝ≥0)

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.99341065
Minimum23
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2021-12-14T23:38:53.229915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile24
Q131
median39
Q347
95-th percentile54
Maximum55
Range32
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.543990289
Coefficient of variation (CV)0.2447590537
Kurtosis-1.203387195
Mean38.99341065
Median Absolute Deviation (MAD)8
Skewness0.003974890882
Sum1775292
Variance91.08775064
MonotonicityNot monotonic
2021-12-14T23:38:53.472890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
401455
 
3.2%
551448
 
3.2%
361420
 
3.1%
231411
 
3.1%
351410
 
3.1%
371409
 
3.1%
491408
 
3.1%
261399
 
3.1%
481396
 
3.1%
271395
 
3.1%
Other values (23)31377
68.9%
ValueCountFrequency (%)
231411
3.1%
241369
3.0%
251373
3.0%
261399
3.1%
271395
3.1%
281368
3.0%
291395
3.1%
301384
3.0%
311373
3.0%
321380
3.0%
ValueCountFrequency (%)
551448
3.2%
541383
3.0%
531385
3.0%
521345
3.0%
511371
3.0%
501377
3.0%
491408
3.1%
481396
3.1%
471314
2.9%
461364
3.0%

gender
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
F
29957 
M
15570 
XNA
 
1

Length

Max length3
Median length1
Mean length1.000043929
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowF
2nd rowM
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
F29957
65.8%
M15570
34.2%
XNA1
 
< 0.1%

Length

2021-12-14T23:38:53.756241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T23:38:53.884054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
f29957
65.8%
m15570
34.2%
xna1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

owns_car
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing547
Missing (%)1.2%
Memory size89.0 KiB
False
29743 
True
15238 
(Missing)
 
547
ValueCountFrequency (%)
False29743
65.3%
True15238
33.5%
(Missing)547
 
1.2%
2021-12-14T23:38:53.981460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

owns_house
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.6 KiB
True
31642 
False
13886 
ValueCountFrequency (%)
True31642
69.5%
False13886
30.5%
2021-12-14T23:38:54.070863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

no_of_children
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing774
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean0.420655137
Minimum0
Maximum9
Zeros31241
Zeros (%)68.6%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2021-12-14T23:38:54.188327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7240971792
Coefficient of variation (CV)1.721355846
Kurtosis3.815248833
Mean0.420655137
Median Absolute Deviation (MAD)0
Skewness1.827606343
Sum18826
Variance0.5243167249
MonotonicityNot monotonic
2021-12-14T23:38:54.346372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
031241
68.6%
18985
 
19.7%
23862
 
8.5%
3584
 
1.3%
460
 
0.1%
513
 
< 0.1%
66
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
(Missing)774
 
1.7%
ValueCountFrequency (%)
031241
68.6%
18985
 
19.7%
23862
 
8.5%
3584
 
1.3%
460
 
0.1%
513
 
< 0.1%
66
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
66
 
< 0.1%
513
 
< 0.1%
460
 
0.1%
3584
 
1.3%
23862
 
8.5%
18985
 
19.7%
031241
68.6%

net_yearly_income
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct45502
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200655.6222
Minimum27170.61
Maximum140759012.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2021-12-14T23:38:54.520997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum27170.61
5-th percentile77893.5085
Q1126345.835
median171714.91
Q3240603.76
95-th percentile392349.715
Maximum140759012.7
Range140731842.1
Interquartile range (IQR)114257.925

Descriptive statistics

Standard deviation669074.0345
Coefficient of variation (CV)3.334439509
Kurtosis42784.72019
Mean200655.6222
Median Absolute Deviation (MAD)54040.965
Skewness203.6835038
Sum9135449170
Variance4.476600636 × 1011
MonotonicityNot monotonic
2021-12-14T23:38:54.937657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174102.442
 
< 0.1%
228141.822
 
< 0.1%
130685.662
 
< 0.1%
136189.922
 
< 0.1%
1289152
 
< 0.1%
249134.472
 
< 0.1%
274014.392
 
< 0.1%
110317.42
 
< 0.1%
151482.742
 
< 0.1%
100156.162
 
< 0.1%
Other values (45492)45508
> 99.9%
ValueCountFrequency (%)
27170.611
< 0.1%
28532.171
< 0.1%
29191.131
< 0.1%
29453.341
< 0.1%
30176.761
< 0.1%
30270.451
< 0.1%
30393.011
< 0.1%
31261.551
< 0.1%
31489.641
< 0.1%
31750.011
< 0.1%
ValueCountFrequency (%)
140759012.71
< 0.1%
4433825.021
< 0.1%
4193101.771
< 0.1%
2784729.581
< 0.1%
2451896.421
< 0.1%
2413494.721
< 0.1%
2408259.111
< 0.1%
2217660.821
< 0.1%
2091355.911
< 0.1%
1947528.141
< 0.1%

no_of_days_employed
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct7874
Distinct (%)17.5%
Missing463
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean67609.28929
Minimum2
Maximum365252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2021-12-14T23:38:55.216882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile227
Q1936
median2224
Q35817
95-th percentile365249
Maximum365252
Range365250
Interquartile range (IQR)4881

Descriptive statistics

Standard deviation139323.5244
Coefficient of variation (CV)2.060715708
Kurtosis0.7827392915
Mean67609.28929
Median Absolute Deviation (MAD)1608
Skewness1.667674633
Sum3046812622
Variance1.941104446 × 1010
MonotonicityNot monotonic
2021-12-14T23:38:55.414508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365246684
 
1.5%
365244669
 
1.5%
365240641
 
1.4%
365245631
 
1.4%
365241628
 
1.4%
365247625
 
1.4%
365250609
 
1.3%
365251607
 
1.3%
365243607
 
1.3%
365252602
 
1.3%
Other values (7864)38762
85.1%
ValueCountFrequency (%)
21
< 0.1%
31
< 0.1%
61
< 0.1%
121
< 0.1%
131
< 0.1%
172
< 0.1%
211
< 0.1%
231
< 0.1%
241
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
365252602
1.3%
365251607
1.3%
365250609
1.3%
365249601
1.3%
365248594
1.3%
365247625
1.4%
365246684
1.5%
365245631
1.4%
365244669
1.5%
365243607
1.3%

occupation_type
Categorical

HIGH CORRELATION

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
Unknown
14299 
Laborers
8134 
Sales staff
4725 
Core staff
4062 
Managers
3168 
Other values (14)
11140 

Length

Max length21
Median length8
Mean length9.444605517
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowLaborers
3rd rowLaborers
4th rowCore staff
5th rowCore staff

Common Values

ValueCountFrequency (%)
Unknown14299
31.4%
Laborers8134
17.9%
Sales staff4725
 
10.4%
Core staff4062
 
8.9%
Managers3168
 
7.0%
Drivers2747
 
6.0%
High skill tech staff1682
 
3.7%
Accountants1474
 
3.2%
Medicine staff1275
 
2.8%
Security staff1025
 
2.3%
Other values (9)2937
 
6.5%

Length

2021-12-14T23:38:55.644181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
staff15070
23.3%
unknown14299
22.1%
laborers8470
13.1%
sales4725
 
7.3%
core4062
 
6.3%
managers3168
 
4.9%
drivers2747
 
4.2%
high1682
 
2.6%
skill1682
 
2.6%
tech1682
 
2.6%
Other values (14)7199
11.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

total_family_members
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing83
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2.158081197
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2021-12-14T23:38:55.786556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum10
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9115724693
Coefficient of variation (CV)0.4223995235
Kurtosis1.335041586
Mean2.158081197
Median Absolute Deviation (MAD)0
Skewness0.9248241035
Sum98074
Variance0.8309643667
MonotonicityNot monotonic
2021-12-14T23:38:55.913778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
223455
51.5%
19913
21.8%
37812
 
17.2%
43623
 
8.0%
5564
 
1.2%
657
 
0.1%
712
 
< 0.1%
86
 
< 0.1%
102
 
< 0.1%
91
 
< 0.1%
(Missing)83
 
0.2%
ValueCountFrequency (%)
19913
21.8%
223455
51.5%
37812
 
17.2%
43623
 
8.0%
5564
 
1.2%
657
 
0.1%
712
 
< 0.1%
86
 
< 0.1%
91
 
< 0.1%
102
 
< 0.1%
ValueCountFrequency (%)
102
 
< 0.1%
91
 
< 0.1%
86
 
< 0.1%
712
 
< 0.1%
657
 
0.1%
5564
 
1.2%
43623
 
8.0%
37812
 
17.2%
223455
51.5%
19913
21.8%

migrant_worker
Categorical

Distinct2
Distinct (%)< 0.1%
Missing87
Missing (%)0.2%
Memory size2.6 MiB
0.0
37302 
1.0
8139 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.037302
81.9%
1.08139
 
17.9%
(Missing)87
 
0.2%

Length

2021-12-14T23:38:56.051255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T23:38:56.137573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.037302
82.1%
1.08139
 
17.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

yearly_debt_payments
Real number (ℝ≥0)

Distinct45251
Distinct (%)99.6%
Missing95
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean31796.96531
Minimum2237.47
Maximum328112.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2021-12-14T23:38:56.282306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2237.47
5-th percentile10354.064
Q119231.14
median29081.65
Q340561.15
95-th percentile62796.392
Maximum328112.86
Range325875.39
Interquartile range (IQR)21330.01

Descriptive statistics

Standard deviation17269.72723
Coefficient of variation (CV)0.5431250141
Kurtosis9.772470795
Mean31796.96531
Median Absolute Deviation (MAD)10514.01
Skewness1.721201453
Sum1444631525
Variance298243478.7
MonotonicityNot monotonic
2021-12-14T23:38:56.605746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23213.372
 
< 0.1%
14243.922
 
< 0.1%
42710.332
 
< 0.1%
31606.522
 
< 0.1%
22534.212
 
< 0.1%
30683.362
 
< 0.1%
39822.632
 
< 0.1%
25862.022
 
< 0.1%
38694.12
 
< 0.1%
17794.722
 
< 0.1%
Other values (45241)45413
99.7%
(Missing)95
 
0.2%
ValueCountFrequency (%)
2237.471
< 0.1%
2752.111
< 0.1%
2881.981
< 0.1%
3139.061
< 0.1%
3230.671
< 0.1%
3256.331
< 0.1%
3324.481
< 0.1%
3328.381
< 0.1%
3369.881
< 0.1%
3506.051
< 0.1%
ValueCountFrequency (%)
328112.861
< 0.1%
279269.561
< 0.1%
276512.941
< 0.1%
274939.591
< 0.1%
255108.891
< 0.1%
231222.571
< 0.1%
221725.741
< 0.1%
219235.541
< 0.1%
213002.31
< 0.1%
199119.181
< 0.1%

credit_limit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct45371
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43548.41603
Minimum4003.14
Maximum31129970.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2021-12-14T23:38:56.760626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4003.14
5-th percentile13356.867
Q123973.805
median35688.045
Q353435.7625
95-th percentile94836.2125
Maximum31129970.49
Range31125967.35
Interquartile range (IQR)29461.9575

Descriptive statistics

Standard deviation148784.6869
Coefficient of variation (CV)3.416534984
Kurtosis41860.88255
Mean43548.41603
Median Absolute Deviation (MAD)13778.625
Skewness200.3871671
Sum1982672285
Variance2.213688305 × 1010
MonotonicityNot monotonic
2021-12-14T23:38:56.905348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33429.222
 
< 0.1%
27557.562
 
< 0.1%
42343.392
 
< 0.1%
47321.452
 
< 0.1%
20353.392
 
< 0.1%
24673.12
 
< 0.1%
17772.022
 
< 0.1%
37674.22
 
< 0.1%
21793.672
 
< 0.1%
44933.962
 
< 0.1%
Other values (45361)45508
> 99.9%
ValueCountFrequency (%)
4003.141
< 0.1%
4030.681
< 0.1%
4042.771
< 0.1%
4210.961
< 0.1%
4219.321
< 0.1%
4328.611
< 0.1%
4328.991
< 0.1%
4356.151
< 0.1%
4483.551
< 0.1%
4601.31
< 0.1%
ValueCountFrequency (%)
31129970.491
< 0.1%
1015611.881
< 0.1%
841596.191
< 0.1%
817713.831
< 0.1%
648100.991
< 0.1%
621953.641
< 0.1%
610931.71
< 0.1%
580567.351
< 0.1%
548115.451
< 0.1%
477006.231
< 0.1%

credit_limit_used(%)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct100
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.23502021
Minimum0
Maximum99
Zeros430
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2021-12-14T23:38:57.052012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q127
median54
Q378
95-th percentile95
Maximum99
Range99
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.3769096
Coefficient of variation (CV)0.562398741
Kurtosis-1.243307626
Mean52.23502021
Median Absolute Deviation (MAD)26
Skewness-0.1274493369
Sum2378156
Variance863.0028178
MonotonicityNot monotonic
2021-12-14T23:38:57.173877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90584
 
1.3%
81571
 
1.3%
87569
 
1.2%
89568
 
1.2%
84566
 
1.2%
78564
 
1.2%
72558
 
1.2%
80557
 
1.2%
99556
 
1.2%
85554
 
1.2%
Other values (90)39881
87.6%
ValueCountFrequency (%)
0430
0.9%
1389
0.9%
2433
1.0%
3431
0.9%
4411
0.9%
5439
1.0%
6437
1.0%
7423
0.9%
8429
0.9%
9425
0.9%
ValueCountFrequency (%)
99556
1.2%
98513
1.1%
97540
1.2%
96524
1.2%
95522
1.1%
94472
1.0%
93507
1.1%
92549
1.2%
91513
1.1%
90584
1.3%

credit_score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct450
Distinct (%)1.0%
Missing8
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean782.7912566
Minimum500
Maximum949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size355.8 KiB
2021-12-14T23:38:57.308422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile621.95
Q1704
median786
Q3867
95-th percentile933
Maximum949
Range449
Interquartile range (IQR)163

Descriptive statistics

Standard deviation100.6197458
Coefficient of variation (CV)0.1285396904
Kurtosis-0.5473691428
Mean782.7912566
Median Absolute Deviation (MAD)81.5
Skewness-0.3025168255
Sum35632658
Variance10124.33323
MonotonicityNot monotonic
2021-12-14T23:38:57.443625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
670186
 
0.4%
660183
 
0.4%
684176
 
0.4%
682175
 
0.4%
651175
 
0.4%
662174
 
0.4%
681172
 
0.4%
691171
 
0.4%
692169
 
0.4%
659168
 
0.4%
Other values (440)43771
96.1%
ValueCountFrequency (%)
50011
< 0.1%
50119
< 0.1%
50221
< 0.1%
50318
< 0.1%
50411
< 0.1%
50517
< 0.1%
50620
< 0.1%
50714
< 0.1%
50820
< 0.1%
50919
< 0.1%
ValueCountFrequency (%)
949146
0.3%
948151
0.3%
947131
0.3%
946138
0.3%
945123
0.3%
944126
0.3%
943146
0.3%
942148
0.3%
941151
0.3%
940139
0.3%

prev_defaults
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
43060 
1
 
2172
2
 
296

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
043060
94.6%
12172
 
4.8%
2296
 
0.7%

Length

2021-12-14T23:38:57.550888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T23:38:57.718731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
043060
94.6%
12172
 
4.8%
2296
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

default_in_last_6months
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
43227 
1
 
2301

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
043227
94.9%
12301
 
5.1%

Length

2021-12-14T23:38:57.779269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T23:38:57.835760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
043227
94.9%
12301
 
5.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

credit_card_default
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0
41831 
1
 
3697

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
041831
91.9%
13697
 
8.1%

Length

2021-12-14T23:38:57.890900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-14T23:38:57.945838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
041831
91.9%
13697
 
8.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-12-14T23:38:47.949099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:34.028812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:35.724572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:37.399123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:39.273431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:41.097528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:42.774977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:44.509961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:46.284819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:48.111126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:34.235247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:35.881626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:37.535578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:39.413926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:41.254943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:42.926885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:44.713040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:46.476135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:48.241621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:34.416049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:35.993527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:37.727963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:39.543822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:41.427431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:43.127055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:44.880710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:46.630328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:48.397255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:34.681403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:36.219116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:37.889731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:39.793424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:41.564787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:43.345783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:45.097491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:46.808526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:48.579727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:34.909614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:36.449226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:38.129775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:39.937951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:41.859508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:43.497831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:45.309410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:47.017183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:48.996866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:35.090734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:36.649441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:38.341810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:40.180326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:42.055302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:43.640515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:45.568947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:47.204841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:49.231174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:35.273870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:36.843839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:38.525465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:40.460455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:42.197748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:43.845646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:45.726980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:47.407135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:49.511251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:35.447097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:37.015198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:38.745533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:40.628881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:42.412104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:44.174216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:45.875061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:47.618685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:49.815156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:35.582605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:37.235556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:38.935135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:40.875729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:42.584722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:44.368052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:46.054742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-12-14T23:38:47.791971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-12-14T23:38:58.015542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-14T23:38:58.197172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-14T23:38:58.379772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-14T23:38:58.549559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-14T23:38:58.716194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-14T23:38:50.480544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-14T23:38:51.405636image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-12-14T23:38:52.020572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-12-14T23:38:52.314639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

customer_idnameagegenderowns_carowns_houseno_of_childrennet_yearly_incomeno_of_days_employedoccupation_typetotal_family_membersmigrant_workeryearly_debt_paymentscredit_limitcredit_limit_used(%)credit_scoreprev_defaultsdefault_in_last_6monthscredit_card_default
0CST_115179ita Bose46FNY0.00107934.04612.00Unknown1.001.0033070.2818690.9373544.00211
1CST_121920Alper Jonathan29MNY0.00109862.622771.00Laborers2.000.0015329.5337745.1952857.00000
2CST_109330Umesh Desai37MNY0.00230153.17204.00Laborers2.000.0048416.6041598.3643650.00000
3CST_128288Rie39FNY0.00122325.8211941.00Core staff2.000.0022574.3632627.7620754.00000
4CST_151355McCool46MYY0.00387286.001459.00Core staff1.000.0038282.9552950.6475927.00000
5CST_123268Sarah Marsh46FYN0.00252765.912898.00Accountants2.001.0037046.8640245.6419937.00000
6CST_127502Mason38MNY1.00262389.205541.00High skill tech staff3.000.0050839.3941311.0842733.00000
7CST_151722Saba46FYY1.00241211.391448.00Core staff3.000.0030008.4632209.2291906.00000
8CST_133768Ashutosh40FNaNY0.00210091.4311551.00Laborers2.000.0021521.8965037.7414783.00000
9CST_111670David Milliken39FYY2.00207109.132791.00High skill tech staff4.000.009509.1028425.5214666.00000

Last rows

customer_idnameagegenderowns_carowns_houseno_of_childrennet_yearly_incomeno_of_days_employedoccupation_typetotal_family_membersmigrant_workeryearly_debt_paymentscredit_limitcredit_limit_used(%)credit_scoreprev_defaultsdefault_in_last_6monthscredit_card_default
45518CST_112481David Bailey54FNY2.00252941.682266.00Laborers3.000.0037513.3845775.4371677.00001
45519CST_144544Emily41MYY2.00293494.24794.00Core staff4.000.0040402.3783883.3797665.00000
45520CST_137966Vladimir47MYY0.00291628.761677.00Drivers1.000.0015627.7342980.8285915.00000
45521CST_161068Dye48FNY0.0089435.47365249.00Unknown2.000.0031233.8821850.7736879.00000
45522CST_120545Tim Hepher54FNN1.00138001.12161.00Unknown3.000.0016609.0418565.6371893.00000
45523CST_130421Doris55FNN2.0096207.57117.00Unknown4.000.0011229.5429663.8382907.00000
45524CST_136670Luciana31FNY0.00383476.74966.00Accountants2.001.0043369.91139947.1632679.00000
45525CST_145435Jessica27FNY0.00260052.181420.00Core staff2.000.0022707.5183961.8346727.00000
45526CST_130913Tessa32MYN0.00157363.042457.00Laborers2.000.0020150.1025538.7292805.00000
45527CST_160078Gopinath38MNY1.00316896.281210.00Unknown3.000.0034603.7836630.7626682.00000